The creation of melodies, arrangements, and even complete tracks using neural networks has become a true breakthrough, offering musicians, sound engineers, and enthusiasts unlimited possibilities. Not long ago, automation mainly involved sound processing (EQs, auto-tune, etc.), but now AI-based tools have learned to create entirely new compositions that are hard to distinguish from those produced in a professional studio.
However, this boom raises many questions, the most pressing of which concern copyright. Who is the “author” of music generated by artificial intelligence? Can a fully automated process be considered creative? And what should be done with music used to train a neural network without the copyright holder’s consent? All these issues are already sparking debates among lawyers, record labels, independent artists, and software developers.
1. How is Music Generated with AI
Before discussing rights, it is important to understand the general principle behind modern music generators and to mention that at the end of this article you will find links to 7 useful services for creating music with AI. So, let’s get started.
Principles of AI models for music generation:
- Data Collection and Preparation.
To train a neural network, developers collect a large number of tracks, samples, or MIDI files. These data are then “cleaned” and categorized – sorted by genre, tempo, instrument, etc. - Model Training.
After preparing the dataset, the neural network undergoes a training phase, analyzing the patterns and structures of melody, harmony, and rhythm. Depending on the algorithm (for example, LSTM or transformers), the results can vary significantly. - Generation of New Content.
The user sets certain parameters (genre, desired tempo, set of instruments) or even creates a text-based “prompt,” and the AI, based on the learned samples, produces its own version of a composition. All this can happen in seconds without complex technical intervention by a person. - Refinement and Post-Processing.
Often, the artificial intelligence generates only the “skeleton” of the work – a melody or rhythmic structure. Musicians then have to fine-tune the track manually – adjusting timbres, correcting some notes, adding special effects.
Popular services offer various approaches: some specialize in auto-generating vocals, some in creating “background” parts, and others can even “imitate” famous performers. And herein lies the main conflict: where is the line between acceptable inspiration and outright copying?
2. The Copyright Issue: Key Questions
As soon as a generated track appears in the public domain, the perennial question arises: who owns the composition? In practice, copyright law covers two main components of a musical work:
- The Musical Idea (Composition): harmony, melody, tempo, structure.
- The Sound Recording (Master Recording): the final file, “performed” or “played” by instruments (similarly to vocals or an orchestra).
If a person creates a track with their own hands (or via a commissioned composer), everything is clear: the author is a human. But with AI generation, things get more complicated:
- Can AI Be Considered the “Author”?
Most current laws do not recognize a machine as a subject of copyright. So even if an algorithm creates a composition entirely without any creative human input, legally, an “author” in the traditional sense does not exist. - The Human Factor
Sometimes a user claims, “I set the parameters, edited the track, so I am the author.” But where is the line at which a person’s contribution is sufficient to be recognized as the author of the composition? - Similarity of Melodies
Generative systems can accidentally create music that closely resembles existing compositions. Is this plagiarism? Lawyers refer to “substantial similarity,” but it is difficult to prove something against an algorithm.
3. Analysis of Policies and Real Cases
The most disputes arise from the fact that laws and platforms often cannot keep up with technological advances. Services like YouTube use a Content ID system – a system that “prints” existing tracks and looks for copies. However, with AI content, the following situations may occur:
- False Blockages
If a new composition generated by AI “resembles” an old song by 70%, the Content ID system might confuse them. The video might be blocked or monetization may go to someone who did not really contribute. - Lack of Precedents
Although there are more and more cases where labels or authors try to challenge the use of AI tracks, there is little legal basis. Most disputes end up as complaints or internal platform reviews. - “Hidden” Authorship
There are instances where people publish completely AI-generated compositions on streaming platforms but claim them as their own and receive royalties. In such cases, platforms may block the artist, although it is difficult in practice to determine the fact of automatic generation. - Moral Dilemma: Using Copyrighted Tracks to “Train” Neural Networks
A separate and very painful issue: most AI models have been trained on enormous music libraries consisting of thousands or even millions of compositions. Did the authors of these tracks give consent for such use?a) Violation of Intellectual Property
From a legal point of view, if the tracks were not publicly available or free for training, this might be a questionable step. At the same time, companies that developed the AI often refer to the policy of “fair use,” claiming that the models only “analyze” the data rather than copying it.b) Dataset Transparency
Musicians are beginning to demand disclosure of information about which tracks were used as the basis for the models. Without such transparency, it is difficult to determine whether specific copyrighted works were used in violation of the law.c) Possible Compensation
Some experts propose introducing licensing agreements: a company that intends to use a large library for AI training should pay the copyright holders. However, so far very few clear mechanisms have been implemented.
4. Tips for Those Who Want to Use AI Music
Despite all the risks, AI tools can be very useful if approached with caution. Here are a few tips:
- Use Reputable Services
Some platforms officially declare that their music is “royalty-free” and that they did not violate any rights when forming their libraries (although guarantees are limited). - Read the License Agreement Carefully
If a service states that “the user is solely responsible for complying with copyright law,” be prepared for potential complaints. It is better to choose platforms with more transparent terms. - Keep Evidence of the Track Creation
Save the “generation log,” screenshots, or service logs so that in case of any claims you have evidence that you did not copy someone else’s melody. - Further Process AI Tracks
Often it is advised to “tweak” the result: change the structure, instrumentation, or key. This gives you a chance to avoid unwanted similarities with existing compositions.
5. Conclusion
Currently, the legislation cannot keep pace with the rapid technological development, making the situation resemble a kind of “Wild West” in the music sphere. On one hand, composers and labels are worried about their rights. On the other, users have a powerful tool for creativity. Until clear legal mechanisms are introduced, it is wise to proceed cautiously: choose reliable AI platforms, check licenses, understand the specifics of copyright law in your country, and consider potential risks.
Artificial intelligence is only a tool that can complement human creative energy but should never completely replace it. Perhaps in the near future, new precedents and laws will emerge to regulate “AI collisions” in music. Until then, we remain witnesses to an interesting and sometimes contentious phase in the history of sound recording, where every musician and listener must find a balance between the convenience of technology and respect for the artistic legacy.
6. Bonus! 7 Services for Creating Music with AI
- Suno (formerly sunno)Link: https://www.sunomusic.ai
Description: Allows you to create short compositions in various genres using simple “prompts.” It is often used for experimenting with different styles.
Pricing: Mostly free access with basic limitations. Paid plans are available (starting at $9.99/month) with an increased number of generations.
My Comment: Features a user-friendly interface, though sometimes there are “glitches” in genre accuracy. Good for demo tracks and simple background clips. - SoundrawLink: https://soundraw.io
Description: Generates musical fragments based on genre, mood, and tempo. It offers ready-made templates for various media projects.
Pricing: Free demo version with limitations, full access starting at $16/month.
My Comment: Convenient for creating background tracks for videos. However, the free version has significant restrictions on track length and quantity. - AIVA (Artificial Intelligence Virtual Artist)Link: https://www.aiva.ai
Description: One of the most renowned platforms for creating orchestral or cinematic compositions. Users can set desired instruments and duration.
Pricing: Free for small projects (up to 3 downloads per month), with plans starting at €11/month.
My Comment: Excels at producing epic and orchestral tracks, though it sometimes lacks “artistic” diversity for modern electronic music. - Ecrett MusicLink: https://www.ecrettmusic.com
Description: Generates background music for videos and games. The user selects the project type, emotion, tempo, and duration.
Pricing: Free plan with an “Ecrett” watermark and limitations, paid plans starting at $5/month.
My Comment: A decent option for content creators on social media, although the originality of tracks is often “mediocre.” - BoomyLink: https://boomy.com
Description: Offers a “one-click track creation” feature. It generates patterns, rhythms, and timbres, with the option for the user to make further edits.
Pricing: Starter plan is free (up to 5 tracks), paid plans starting at $14.99/month.
My Comment: User-friendly for beginners, though sometimes the sound comes out “too mechanical.” Suitable for quick, experimental scenarios. - SoundfulLink: https://soundful.com
Description: Based on a neural network that generates “ready-to-use” music for ads, podcasts, and YouTube videos. It offers a variety of genres and styles.
Pricing: Free plan with access to a limited library, Pro plans starting at $9.99/month.
My Comment: Notable for its easy integration with video editors. However, some tracks may sound quite similar to each other. - Epidemic Sound (AI Extension)Link: https://www.epidemicsound.com
Description: Although Epidemic Sound is known as a stock music library, it has begun testing AI tools for the quick selection and generation of tracks. The service is still evolving.
Pricing: Starting at $9/month (subscription), with AI features predominantly in a closed beta version.
My Comment: It’s interesting that a well-known music platform is also moving toward artificial intelligence. We look forward to a stable release, as combining a ready-made library with AI is very promising.
General Note. Almost all these services allow you to generate music under a free plan or demo version, but with certain limitations (watermarks, limited number of tracks, lower quality, or lack of a commercial license). If you plan to use the music in your public or commercial projects, carefully review the terms of use and licensing to avoid copyright issues.
